Method for inspecting composite structures using quantitative infra-red thermography

ABSTRACT

A system and method for inspecting a surface of a structure for defects includes an inspection apparatus having a heating device for heating a section of the surface of the structure, an infrared camera for receiving infrared radiation from the surface in response to heating, a controller configured to generate thermographs from the received infrared radiation, and a communication device. A training system includes an expert system module configured to determine correlations between a set of thermographs generated by a thermal simulation of modeled structural elements with defects, and parameters of the modeled structural elements. A computer system communicatively coupled to the training system and the inspection apparatus, is adapted to receive thermographs received from the inspection apparatus and to detect quantitative parameters of defects in the structure using the correlations obtained from the training system.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a Divisional of, and claims priority from, U.S.patent application Ser. No. 16/575,152, titled METHOD FOR INSPECTINGCOMPOSITE STRUCTURES USING QUANTITATIVE INFRA-RED THERMOGRAPHY, filed onSep. 18, 2019, which is a divisional of U.S. patent application Ser. No.15/490,578, titled APPARATUS, SYSTEM AND METHOD FOR INSPECTING COMPOSITESTRUCTURES USING QUANTITATIVE INFRA-RED THERMOGRAPHY, filed on Apr. 18,2017, now granted as U.S. Pat. No. 10,473,603 on Nov. 12, 2019, thecontents of which are hereby incorporated by reference in theirrespective entireties.

FIELD OF THE INVENTION

The present invention relates to material inspection andcharacterization and in particular relates to an apparatus, system andmethod for inspecting composite structures using quantitative infra-redthermography.

BACKGROUND OF THE INVENTION

Composite materials (hereinafter “composites”) are currently used as areplacement for metallic materials in many industrial applicationsbecause of their resistance to corrosion. In the oil and gas industryfor instance, composites are used in filament wound composite structuressuch as pipes and vessel tanks. A pipeline made from composites is shownin FIG. 14 to indicate the scale of composite structures currently usedin the field. While the benefits of corrosion resistance favor theiruse, composites can suffer from susceptibility to other types of damagesuch as impact, creep and aging.

Given the susceptibilities of composites to certain types of damage, itis important to periodically inspect composites to test whether suchdamage has occurred or is accumulating. It is also a requirement for theinspection to be non-destructive because it is infeasible to employinvasive techniques that interrupt the continued operation of thestructures in the field. Suitable non-destructive testing (NDT)techniques should be able to accurately detect typical defects incomposites, be easy to apply, and permit rapid and automated inspectionof large areas. It would also be advantageous for such techniques toprovide in-service inspection with minimum surface preparation.

Among common NDT techniques, infrared thermography stands out as a goodcandidate since it provides contact-free measurement (no need forcoupling media), global and focused area scans, fast acquisition, andeasy operation. Limitations of the sensitivity of infrared thermographyequipment have until now restricted this technique to qualitative andboundary inspections, both of which are unable to provide accuratedefect size, depth data or data on the nature of any entrapped media,and are limited to detecting defects located close to the surfaces ofthe inspected structures.

There is therefore a need for non-destructive techniques for rapidly,reliably and cost-efficiently inspecting composite structures in anaccurate quantitative manner. The present invention is addressed to thisand related needs.

SUMMARY OF THE INVENTION

According to an aspect of the present invention, a system for inspectinga surface of a structure for defects is provided. According to oneembodiment, the system comprises: 1) an inspection apparatus including aheating device for heating a section of the surface of the structure, aninfrared camera for receiving infrared radiation from the surface inresponse to heating, a controller configured to generate thermographsfrom the received infrared radiation, and a communication device; 2) atraining system including an expert system module configured todetermine correlations between a set of thermographs, the thermographsbeing generated by a thermal simulation of modeled structural elementswith defects, and to determine parameters of the modeled structuralelements; and 3) a computer system communicatively coupled to thetraining system and the inspection apparatus, the computer systemadapted to receive thermographs received from the inspection apparatusand to detect quantitative parameters of defects in the structure usingthe correlations obtained from the training system.

In some embodiments, the structure is composed of a composite material.In some implementations, the quantitative parameters detected by thecomputer system include a location, a depth, an orientation, a defecttype, an entrapped media type, or a sub-combination thereof. In furtherimplementations, the expert system module employs an expert system(e.g., a neural network) to determine the correlations between the setof thermographs and the corresponding set of modeled structuralelements. In still further embodiments, a combination of the foregoingcan include a composite material as the structure, with the quantitativeparameters detected by the computer system including a location, adepth, an orientation, a defect type and an entrapped media type, and instill further embodiments this combination can be implemented togetherwith an expert module that employs a neural network as just described.

In some embodiments, the training system further includes a defectmicrostructure database module configured to generate the set of modeledstructural elements, each structural element including an integrateddefect. In some implementations, the training system also includes avirtual thermograph database module configured to perform a thermalanalysis of each of the modeled structural elements and to generatetransient thermographs corresponding to the structural elements. Thethermal analysis can be implemented using finite element analysis.Again, embodiments can be implemented with each or all of the featuresnoted in this paragraph.

In some implementations, the parameters of each of the modeledstructural elements include location, orientation, defect type, defectsize and entrapped media. The modeled defect type can be one ofdelamination, unique void, matrix cracking, fiber-matrix de-bonding,multiple voids, and holes. The modeled entrapped media can be one ofliquid or gas.

In further embodiments of the present invention, the training systemincludes an optimized acquisition parameter module configured toautomatically determine acquisition parameters for controlling theinspection apparatus based on material properties of the structure,environmental conditions, and a thermal analysis of a modeled structuralelement. In some implementations, the acquisition parameters determinedby the optimized acquisition parameter module include a heating time,target heat flux level for operating the heating device, an acquisitiontime for operating the infrared camera of the inspection apparatus, or asub-combination of these features. The thermal analysis can be performedon a modeled structural element having at least one of a smallest and adeepest defect.

In some implementations of the present invention, the computer systemreceives the acquisition parameters from the training system. In suchimplementations, the computer system transmits the acquisitionparameters to the inspection apparatus. In further implementations, theexpert system module employs a neural network to determine thecorrelations between the set of thermographs and the corresponding setof modeled structural elements.

According to another embodiment, the inspection apparatus includes aclamp element for fixing the apparatus to the surface of the structure,and a chassis unit for housing the heating element and infrared camera.In some implementations, the chassis unit is slidingly coupled to theclamp element and rotatable circumferentially with respect to thesurface of the structure. In further implementations, the chassis unitand clamp element are coupled to rotatable and translatable wheels,thereby enabling the inspection apparatus to rotate circumferentiallyand translate longitudinally along the surface of the structure. Forinstance, the chassis unit and clamp element can be coupled to a roboticvehicle.

According to another aspect of the present invention, a system forinspecting a surface of a structure for defects comprises: 1) aninspection apparatus including a heating device for heating a section ofthe surface of the structure, an infrared camera for receiving infraredradiation from the surface in response to heating, a controllerconfigured to generate thermographs from the received infraredradiation, and a communication device; 2), a training system includingan expert system module configured to determine correlations between aset of thermographs and parameters of modeled structural elements, andan optimized acquisition parameter module configured to automaticallydetermine parameters for controlling the inspection apparatus based onmaterial properties of the structure and environmental conditions; and3) a computer system communicatively coupled to the training system andthe inspection apparatus, the computer system being adapted to receivethermographs received from the inspection apparatus and to detectquantitative parameters of defects including an entrapped media type inthe structure using the correlations obtained from the training system.

According to still another aspect, a method of training a system toenable an inspection apparatus to perform an accurate quantitativeinspection of a surface of a structure for defects is provided. In oneembodiment in accordance with this aspect, the method comprisesreceiving operator inputs concerning properties of the structure andenvironmental conditions at the structure, generating a set ofstructural elements using the operator inputs, each of the modeledstructural elements including an integrated defect, generatingthermographs corresponding to each of the structural elements throughapplication of a transient thermal analysis, and computing correlationsbetween the thermographs and the parameters of corresponding structuralelements, wherein the correlations enable thermographs taken ofstructures to be analyzed to determine quantitative parameters ofdefects in the structure. In some embodiments, the structure is composedof a composite material.

In some embodiments of the present invention, the transient thermalanalysis employs finite element analysis. In other embodiments, thegenerated structural elements are characterized by a location,orientation, defect type, defect size, entrapped media, or asub-combination thereof. In some implementations the defect type is oneof delamination, unique void, matrix cracking, fiber-matrix de-bonding,multiple voids, and holes. The entrapped media can be a liquid or gassuch as air, water and oil.

In further embodiments, the method includes determining optimalacquisition parameters for controlling the inspection apparatus based onmaterial properties of the structure, environmental conditions, and athermal analysis of a structural element. In some implementations, theacquisition parameters include a heating time, a target heat flux level,an acquisition time for operating the inspection apparatus, or asub-combination of the foregoing. The thermal analysis can be performedon a structural element having at least one of a smallest and a deepestdefect.

In some implementations, the correlations between the thermographs andthe parameters of corresponding structural elements are determined usinga machine learning technique. In more particular implementations, themachine learning technique employs a neural network.

According to yet another aspect of the present invention, a method ofquantitatively inspecting a surface of a structure for defects, fromwhich infrared thermographs are acquired by an inspection apparatus, isprovided. One method in accordance with this aspect comprises obtaininga set of correlations between parameters of modeled structural defectsand simulated thermographs of the modeled structural defects, andoptimal acquisition parameters for configuring the inspection apparatusfor acquiring thermograph data from the structure, communicating theacquisition parameters to the inspection apparatus, receiving infraredthermograph data acquired from the structure from the inspectionapparatus, analyzing the received thermograph data using the obtainedcorrelations, and determining parameters of defects within the structurebased on the analysis of the received thermograph.

In some embodiments of the method, acquisition parameters arecommunicated to the inspection apparatus. In some embodiments,thermograph data is received from the inspection apparatus via wirelesscommunication. In some embodiments, the parameters of defects within thestructure that are determined include a location, a depth, anorientation, a defect type, an entrapped media type, or asub-combination of the foregoing. In further embodiments, theacquisition parameters for configuration the inspection apparatusinclude a heating time, a target heat flux level for applying heat tothe structure, an acquisition time detecting infrared radiation from thestructure, or a sub-combination thereof. The optimal acquisitionparameters can be determined based on a material of the structure andenvironmental conditions at the structure. More particular embodimentsinclude a combination of the features described in this paragraph.

According to yet another aspect of the present invention, a method ofquantitatively inspecting a surface of a structure using an inspectionapparatus having a heating device and an infrared camera is provided.One method in accordance with this aspect comprises receiving optimalacquisition parameters for configuring the heating element, heating asection of the structure using the heating device according to thereceived acquisition parameters, detecting infrared radiation emittedfrom the section of the structure according to the received acquisitionparameters, generating thermograph data from the detected infraredradiation, and communicating the thermograph data to a computer systemto determine defects of the structure using the thermograph data. Theanalysis employs a set of correlations between parameters of modeledstructural defects and simulated thermographs of the modeled structuraldefects and obtains parameters of corresponding to the receivedthermograph data using the correlations.

In some embodiments, the method further includes removably fixing theinspection apparatus in proximity to the structure using a clampelement. In some implementations, the method includes rotating theheating device and infrared camera of the inspection apparatuscircumferentially around the structure with respect to the clamp elementand can also include translating the clamp element longitudinally overthe structure using at least one wheel. The acquisition parameters caninclude a heating time, a target heat flux level for heating the sectionof the structure, an acquisition time detecting infrared radiation fromthe structure, or a sub-combination thereof. In some implementations,the optimal acquisition parameters are determined based on a material ofthe structure, the environmental conditions at the structure, or both.

According to yet another aspect of the present invention, an apparatusfor inspecting a surface of a structure for defects is provided. Oneembodiment of an apparatus according to this aspect of the comprises: 1)a clamp element for removably fixing the apparatus in proximity to thesurface of the structure; and 2) a chassis unit coupled to the clampelement, the chassis unit housing: i) a heating device configurable toheat a section of the surface of the structure; ii) an infrared cameraconfigurable to acquire infrared radiation from the surface of thestructure; iii) a controller communicatively coupled to and operative tocontrol the heating device and infrared camera; and iv) a transceiver.The controller receives optimal acquisition parameters from a systemthat determines the parameters based on a material of the structure andenvironmental conditions in a proximity of the structure.

In some embodiments, the inspection apparatus further comprises slidingelements coupled between the chassis unit and the clamp element thatenable the chassis unit to rotate along the clamp elementcircumferentially around the structure. In other embodiments, theinspection apparatus further comprises rotatable and translatable wheelsfixed to ends of the clamp element and chassis unit, the wheels enablingthe clamp element and chassis unit to rotate circumferentially andtranslate longitudinal over the surface of the structure. In someimplementations, the optimal acquisition parameters include a heatingtime, a target flux level for applying heat the section of the structureusing the heating device, an acquisition time detecting infraredradiation from the structure using the infrared camera, or asub-combination thereof.

These and other aspects, features, and advantages can be appreciatedfrom the following description of certain embodiments of the inventionand the accompanying drawing figures and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a system for inspecting compositestructures using quantitative infrared thermography according to anexemplary embodiment of the present invention.

FIG. 2A is a perspective view of an exemplary embodiment of aninspection apparatus according to the present invention.

FIG. 2B is a perspective view of another exemplary embodiment of aninspection apparatus according to the present invention that can movecircumferentially around an inspected structure.

FIG. 2C is a perspective view of another exemplary embodiment of aninspection apparatus according to the present invention that can moveboth circumferentially and longitudinally along an inspected structure.

FIG. 3 is a schematic diagram showing the components of an inspectionunit (chassis) of the inspection apparatus according to an exemplaryembodiment of the present invention.

FIG. 4A is a schematic diagram of an embodiment of a heating device andinfrared camera that can be employed in an inspection apparatusaccording to the present invention.

FIG. 4B is a graph showing exemplary activation input (above) andinfrared responses (bottom) according to a pulse thermographyactivation.

FIG. 4C is a graph showing exemplary activation input (above) andinfrared responses (bottom) according to a lock-in thermographyactivation.

FIG. 5 is a flow chart of a method of training an expert system tocorrelate virtual thermographs with characteristics of modeled defects(RVEs) according to an exemplary embodiment of the present invention.

FIG. 6 is schematic flow chart of a method of generating a defectivemicrostructure database (DVDB) according to an exemplary embodiment ofthe present invention.

FIG. 7A is a schematic perspective illustration of a representativevolume element (RVE) according to an exemplary embodiment of the presentinvention.

FIG. 7B is cross-sectional view of the RVE of FIG. 7A taken along axisA.

FIG. 7C is a cross-sectional of the RVE of FIG. 7A taken along axis B.

FIG. 8 is a flow chart of a method of automatically generating optimizedacquisition parameters according to an exemplary embodiment of thepresent invention.

FIG. 9 is a schematic flow chart of a method of generating a virtualthermograph database (VTDB) according to an exemplary embodiment of thepresent invention.

FIG. 10A is a schematic illustration of an exemplary matrix datastructure for storing thermograph data generated by according toembodiments of the present invention.

FIG. 10B is a schematic graphical illustration of an embodiment of thematrix data structure of FIG. 10A for a specific RVE.

FIG. 11 is a schematic diagram of an exemplary neural network that canbe employed in the expert system training method according toembodiments of the present invention.

FIG. 12 is a flow chart of a method for real time inspection of astructure according to an exemplary embodiment of the present invention.

FIG. 13 is a schematic diagram of a process of analyzing an acquiredthermograph using an expert system to yield defect parameters accordingto an exemplary embodiment of the present invention.

FIG. 14 is a photograph of a large pipeline made of composite material.

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS OF THE INVENTION

A systematic approach to reliably and quantitatively inspectingstructures using infrared thermography is disclosed. The approachesdisclosed herein are particularly applicable for inspecting compositematerials. In some embodiments, the inspection system includes threedistinct elements: 1) a training system that a) models structuraldefects of a composite material, b) performs a mathematical simulationof how the modeled defects react to heating and which generates virtualthermographs (images indicative of temperature) showing temperaturechanges of the modeled defects over time, and c) correlates the virtualthermographs with parameters of the modeled defects using a machinelearning approach, producing an accessible virtual thermograph database;2) an inspection apparatus that is used at the site of the structure,and that includes a heating element to apply heat to a section of thestructure surface, and a recording device to record infrared radiationemitted from the heated section of the surface; and 3) an onsitecomputing system that: a) accesses the training system to obtain thecorrelations between the thermographs of the parameters of the defects;b) receives thermographs of recorded infrared radiation from theinspection apparatus; and c) quantitatively determines the parameters ofthe received thermograph using the correlations obtained from thetraining system. Additional details of the system are discussed inreference to the illustrated embodiments.

The disclosed system provides an integrated solution to the problem ofdetecting defects over composite structures with large and/or extendedsurfaces that is easy to implement, provides for fast inspection, and iseconomically efficient.

As a preliminary matter, the terms “thermograph” and “thermogram” areinterchangeable herein and both are to be interpreted as images of asurface area captured by an infrared camera or sensor in which a color,hue, gray scale or other differentiating mark indicates a specifictemperature or temperature range.

Inspection System

Turning to FIG. 1 , an embodiment of a system 100 for inspectingcomposite structures using quantitate infrared thermography is shown.The system 100 includes an inspection apparatus 110 that is positionedproximate to a surface section 115 of a structure, which can be made ofa composite. The apparatus, described in greater detail below, heats thesurface section 115 and detects and records infrared radiation that isemitted from the section 115 in response to being heated. The inspectionapparatus 110 is communicatively coupled, preferably wirelessly, butoptionally by a wired connection, to a computer system 120. Computersystem 120 is operable to receive and process the data recorded by theinspection apparatus and is also communicatively coupled to a trainingsystem 130. The computer system 120 uses the data received from theinspection apparatus 110 and correlation information received from thetraining system 130 as inputs to a defect identification andquantification module (IDQ) 122, which generates a defect quantificationreport providing the type, size, depth, orientation and entrapped mediainformation for any defect identified on the structure surface. Thecomputer system 120 can be implemented onsite using any computing devicehaving sufficient processing and memory resources (e.g., a single ormulticore processor and solid-state memory), including a laptop, tabletor any other computing device readily accessible during an onsiteinspection.

The training system 130 includes at least one processor that isoperative to execute several modules. As will be described in greaterdetail below, the modules include a defect microstructure database(DMDB) module 132 that comprises code that causes the at least oneprocessor to use relevant inputs to generate a set of modeled structuraldefects, each defect of the database having a specific type, size,depth, orientation and entrapped media. The defects are stored in anassociated DMDB database. The training system 130 also includes avirtual thermograph database (VTDB) module 134 that comprises code thatcauses the at least one processor to run mathematical simulations whichcalculate expected responses of the microstructure defects within theDMDB database 132 to heating, and which causes the at least oneprocessor to generate virtual thermographs of the expected infraredradiation emissions from each of the microstructures. The virtualthermographs are stored in a VTDB database. The training system 130 alsoincludes an expert system module 136 that executes a machine learningalgorithm as may be implemented in the processor (e.g., as computercode), such as a neural network, to correlate the virtual thermographsoutput by the VTDB module 134 with the parameters of the defects in theDMDB database 132. An optimized acquisition parameter (OAP) module 138comprises code that causes the at least one processor to automaticallydetermine optimal parameters for controlling the inspection apparatus110 including optimal heating parameters such as heating mode, heatingtime, acquisition time, heat flux, etc. based on inputs including theproperties of the inspected composite material and environmental andoperating conditions. Modules 132, 134, 136, 138 can include and/or makeuse of processing resources for executing computer program instructionswhich generate data, and also employ memory resources for storing thegenerated data. All of the processes executed by training system 130 canbe executed before an inspection of an actual structure.

The computing resources allocated for the training system 130 can beco-located on a single computing system or at a single facility or,alternatively, can be distributed across multiple computing systems andat a single or multiple facilities. Additionally, the training systemcan be hosted on fixed systems or can be hosted on the cloud on avirtual computing platform. In certain embodiments, distributedcomputing resources implement code that cause one or more of thecomputing resources to pause or cease one or more operations as afunction of the operational state or particular data of another one ofthe computing resources. In such an embodiment, computational resourcesare preserved by controlling operations in response to coordinatedcommunications among such resources in view of operational state updatesor particular data.

Inspection Apparatus

FIG. 2A is a perspective view of an embodiment of an inspectionapparatus 200 according to the principles disclosed herein. Theapparatus 200 is shown affixed to a pipe structure 205 made of acomposite that is to be inspected. Apparatus 200 includes adjustablesupporting clamps 210, 220 that are used to firmly and removablyposition and affix the apparatus 200 at a desired position on thestructure 205 to inspect a particular surface section. Clamps 210, 220are curved to adapt to structures having different circumferences. Theends of clamps 210, 220 terminate at respective suction pads e.g., 212,222 (pads on the reverse side of the structure 205 are not shown) orother suitable mechanism for firmly and removably affixing the clampends to the surface of the structure 205. A semi-enclosed chassis unit230 is coupled to and positioned between the clamps 210, 220. In theembodiment depicted the chassis unit 230 includes the components usedfor inspection as will be described further below. The chassis unit 230can be fixedly attached to the clamps 210, 220 by bar elements as shown,or alternatively, chassis unit 230 can be removably coupled to theclamps in other implementations.

FIG. 2B is a perspective view of another embodiment of an inspectionapparatus 250 according to the principles disclosed herein. In thisembodiment, clamps are replaced with slide guides 255, 260 that extendfurther around structure 205 and similarly end in respective suctionpads, e.g., 257, 262. A chassis unit 265 including the components usedfor inspection is coupled on first and second sides to sliding elements270, 275. In the embodiment depicted, the sliding elements 270, 275 areimplemented as semicircular-shaped components, each having a groove withwhich they mate to respective slide guides 255, 260. Sliding elements270, 275 include respective sets of wheels 272, 277 with which theymovably grip the surface of substrate 205. As depicted, the chassis unit265 is coupled to sliding elements which are movable circumferentiallyaround the structure as constrained by the slide guides 255, 260. Thisallows the chassis unit 265 to be carried circumferentially by movementof the sliding elements 270, 275. Wheels 272, 277 can be moved eithermanually or remotely (electronically) to actuate the sliding motion, andthe inspection apparatus 250 can be moved automatically around thecircumference to inspect a number of sections on the surface of thesubstrate sequentially. This enables the operator to scan large areas ofthe structure 205 while employing a single configuration and setup ofthe inspection apparatus 250.

FIG. 2C is a perspective view of a further embodiment of an inspectionapparatus 280 according to the present invention that provides bothcircumferential (rotational) and longitudinal movement (translation) ofthe apparatus 280 along a structure 205. Chassis 282 is coupled oneither side to sliding elements 284, 285. Spring elements 286, 287 areattached to sliding element 284 and springs elements 288, 289 areattached to the sliding element 285. Spring elements 286-289 can beimplemented using torsion springs. Springs 286-289 aid in fixing thechassis 282 at a specific position on structure 205 during aninspection. Latching arms 291, 292 are pivotably coupled to slidingelement 284, and latching arms 293, 294 are pivotably coupled to slidingelement 285. Wheels, e.g. 296, 297 are coupled to the bottom ofrespective sliding elements 284, 285 and wheels, e.g., 298, 299 arecoupled to the distal ends of latching arms 291, 292, 293, 294. Thewheels, e.g., 296-299 are preferably implemented using Omniwheels thatcan both slide and rotate on their axes enabling the apparatus 280 to bemoved either manually or remotely (electronically) in bothcircumferential and longitudinal directions with respect to SaudiAramco: Confidential structure 205. This embodiment also enables theoperator to scan large areas of the structure 205 while employing asingle configuration and setup of the inspection apparatus 250.

FIG. 3 is a schematic diagram showing an embodiment of the components ofa chassis unit 300 that can be implemented in the apparatuses 200, 250,280 to perform the inspection of the composite structure by activeinfrared thermography. Active thermography involves heating the surfaceof an inspected area to create a difference between the temperature ofthe surface immediately above the defect and the surroundingtemperature. The heating produces an internal heat flux within a certaindepth of the surface. Subsurface defects affect the heat diffusion andproduce corresponding thermal contrasts which are reflected in theinfrared radiation emitted from the surface. Defects, which block andslow diffusion of heat within the material, are detected by the mannerin which the captured infrared radiation changes over time. Typically,sub-surface defects cause the surface immediately above the defect tocool at a different rate than the surrounding areas.

Turning to FIG. 3 , the chassis unit 300 is semi-enclosed and the sideof the unit that faces the structure surface is open at least in part topermit a heating device 310 and an infrared camera 320 to extendoutwardly from the enclosure of the housing toward the surface. Theheating device 310 is operable to emit radiation toward a section of thesurface during an inspection. An infrared camera 320 is operable todetect infrared radiation emitted back from the surface in response toheating. Both the heating device 310 and the infrared camera 320 operateat a distance from the surface of the structure. FIG. 4A is a schematicillustration of one implementation of the heating device 310 andinfrared camera 320. In this figure the heating device 310 comprises twoheating lamps 405, 410 arranged adjacent to one another so as to emit acone of radiation 412 to cover an area of surface of a structure 415.The radiation causes a heat flux 420 beneath the surface of structure415, and infrared camera 320 is positioned centrally to receive anoptimal intensity of infrared radiation 425 emitted from the surface. Anexemplary defect 430 is shown located at a depth beneath surface 415.The heating device 310 can also include a hood 435 as a protectionagainst the intense radiation emitted by the heating device 310. Theinfrared camera 320 is adjustable to optimize acquisition of emittedinfrared radiation and can be positioned centrally between the heatinglamps 405, 410 (as shown in FIG. 4A) or adjacent to the heating elementsas shown in FIG. 3 , and oriented at various angles with respect to thesurface of the inspected structure.

Referring again to FIG. 3 , chassis unit 300 also includes a controller330 (e.g., a microcontroller or processor) operative to control theheating device 310 and infrared camera 320. Controller 330 is alsocoupled to a memory unit 340 and to a transceiver 350 with which it iscommunicatively coupled to computer system 120 (of FIG. 1 ). Transceiver350 can conduct communication using various communication modesincluding Wi-Fi, RF and Zigbee protocols to achieve two-way datatransmission between the inspection apparatus and online computer system120.

Heating lamps used for infrared thermography typically employ xenonflashtubes. During operation, lamps 405, 410 produce flashes of light inresponse to trigger signals from controller 330. After activating thelamps 405, 410, the controller 330 activates the infrared camera 320 toperiodically capture successive digital images of the radiativeemissions of the heated portion of the inspected surface. The infraredcamera 320 can be coupled to a motor operated by controller 330 tochange the angle and distance between the camera and the inspectedsurface to achieve a suitable focus on the surface. The digital imagedata generated by the infrared camera 320 can be transferred to andstored in memory unit 340. The controller 330 utilizes transceiver 350to transfer the digital image data from the memory unit 340 to computersystem 120. The controller 330 can also perform some pre-processing ofthe digital image data prior to transmission to computer system 120. Forexample, as the inspection apparatus is moved and images are capturedfrom adjacent surface sections, the controller 330 can format the datainto discrete image frames. Alternatively, such preliminary imageprocessing can be performed at computer system 120.

Among several active infrared known infrared thermography excitationmethods, pulsed thermography and lock-in thermography have been widelyused. FIG. 4B is a graph of the amplitude (intensity) of activationradiation provided over time (above), and amplitude of infraredradiation emitted from the surface over time (below). As indicated, inpulse thermography a pulse of high energy over short-duration is appliedto a surface and the amplitude of infrared radiation emitted back fromthe surface rises sharply in response, and then starts to fall as soonas the activation pulse ends. Presence of a defect is indicated by therelatively slower rate at which the amplitude of infrared radiationemitted from the surface declines (i.e., the slower rate at which thesurface cools). FIG. 4C is a similar graph of amplitude versus timeshowing a continuous, e.g., sinusoidal activation and a correspondingsinusoidal infrared response. As indicated, in lock-in thermography,presence of a defect is not shown in a different in amplitude response,but rather in a phase shift between the input activation energy and thesurface temperature response. The phase analysis of lock-in thermographyhas the advantage of being less sensitive to the local variations ofillumination or surface emissivity in comparison to pulsed thermography.However, either or both of pulsed and lock-in thermography as well asother excitation methods can be used.

As inspection of the composite structure is performed, with periodicheat activation and acquisition of infrared image data, the controller330 preferably receives and transfers the digital image data in realtime wirelessly as a video stream to computer system 120 for analysisand identification of defects.

Themography Training Method

Before turning to the analysis of the data acquired by the inspectionapparatus, we turn first to a description of the inventive trainingmethod which enables the analysis to achieve accurate quantitative dataconcerning defects in a structure. FIG. 5 is a schematic flow chart ofan embodiment of the training method 500 as disclosed herein. Thetraining method includes several distinct procedures: i) input ofrelevant data by an operator via a user interface (510); ii) automaticconfiguration of internal parameters (520); iii) generation of adatabase of representative microstructures (DMDB) with integrateddefects (530); iv) determination of optimal setup parameters of theinspection apparatus for data acquisition (540); v) generation of avirtual thermograph database (VTDB) by simulation (550); and vi)training of an expert system to determine correlations between themicrostructures of the DMDB and the thermographs of the VTDB generatedby simulation (560). Each of procedures (i) to (vi) are described inturn. It is noted, however, that in alternative embodiments, a subset ofthese procedures can be performed without departing from the principlesdisclosed herein.

FIG. 6 is a schematic view of an embodiment of the first threeprocedures 510, 520, 530 of the training method outlined above. Asdepicted in step 510, inputs including material, structural andenvironmental properties are entered into the training system 130 by anoperator in order to model and store a set of representativemicrostructures containing specific defects. The possible materialproperties include parameters, such as, but not limited to: resin andfiber thermal conductivity, specific heat, fiber volume content,fraction of porosity, ply thickness, layup sequence, fiber orientationper ply, internal and/or external coating thickness. Input structuralproperties include the diameter and thickness of the material.Environmental and operating properties include parameters, such as, butnot limited to: operating pressure, transported fluid temperature andflow velocity, ambient temperature, and high temperature points inproximity to the inspected structure. In addition, the operator inputsset the defect type and entrapped media for each microstructure. Thepossible defect types include, among others: delamination, unique void,matrix cracking, fiber-matrix de-bonding, multiple voids, and holes.Entrapped media constitutes fluid or gas entrapped within the defects,which are typically air, water or oil. The parameters set forth areexemplary and do not constitute an exhaustive listing of all parametersor types that can be entered into the training system by operators.

In addition to the parameters entered by operators of the trainingsystem, the training system generates internal parameters in step 520.The internal parameters are used to initialize and configure a thermalsimulation model and can include, among other internal parameters, aselection from among: heat flux over the material surface over time,increments for defect size, depth location, minimum and maximum defectsize, minimum and maximum out-of-plane size, minimum and maximum depth,mesh discretization, and other thresholds for setting bounds on theparameters of defects. The internal parameters can be modifiable by theoperator.

The defect microstructure database (DMDB) module 132 uses the operatorinput and internally generates parameters, in step 530, to generate adatabase (DMDB) 605 that includes a number (N) of models of smallstructural elements, referred to herein as microstructures, e.g. 610,612, with each microstructure having specific parameters and at leastone integrated defect. The number (N) can also be controlled by theoperator through control over increment sizes. In some implementations,N is in a range of 1,000 to 50,000. However, a greater or smaller numberof microstructures can be generated. Each entry of the database, termeda “representative volume element” (RVE) can be parameterized as a vectorof eight elements V_(k)[a_(k), b_(k), c_(k), z_(k), θ_(k), φ_(k), D_(k),M_(k)] where z_(k) is the coordinate of the defect centroid in theout-of-plane direction (perpendicular to the inspection plane) in thekth RVE, a_(k), b_(k) and c_(k) are the spatial dimensions of the defectwithin the kth RVE, θ_(k) and φ_(k) are the angles between the plane ofthe defect and the inspection plane, D_(k) is the defect type, and M_(k)is the type of media entrapped within the defect. FIG. 7A is a schematicrepresentation of an example RVE defect stored in the defectmicrostructure database (DMDB). The defect 700 is modeled as anellipsoid in which z_(k) defines the location of the center of thedefect across the composite thickness, a_(k), b_(k), c_(k) define thelength, width and thickness of the defect and angles θ_(k) and ϕ_(k) incross-sectional planes A and B define the position and orientation ofthe defect with respect to the surface of the composite structure (theinspection plane). In the example depicted in FIG. 7A, the defect is anisolated delamination which is indicated by parameter D_(k), and theentrapped media is air, indicated by parameter M_(k).

While the model simplifies the geometry of defects to some extent, thelarge number and variation in location, sizes, defect types andentrapped media generated in practice cover and suitably representtypical defects that occur in composite structures. FIG. 7B is across-sectional view of RVE 700 taken along axis A showing firstorientation angle θ_(k) of the RVE with respect to the inspection plane.FIG. 7C is an analogous cross-section view of RVE 700 taken along axis Bshowing a second orientation angle #k of the RVE with respect to theinspection plane.

In step 540 of the training method 500, the optimized acquisitionparameter (OAP) module 138 uses the operator input including materialproperties and operating conditions as well as internally generatedparameters to determine optimal infrared thermography parameters forconfiguring an inspection apparatus. FIG. 8 is a flow chart of the OAPdetermination method 540. In a first step 810, the DMDB is searched andthe RVE that has the smallest and/or deepest defect is selected. In step820, the OAP module 138 determines initial and boundary conditions for athermal simulation model of the selected RVE using the initialparameters, which here are the parameters for heating flux (ΔH_(f)),heating period (ΔH_(p)), heating mode (e.g., continuous, modulated,pulsed) and camera acquisition time (Δt) generated in step 520 of thetraining method. However, it is noted that the heating parameters willdepend on the heating mode (e.g., flash, pulse, continuous). Forexample, in pulse mode, the frequency of the heating pulse will be acontrolled parameter.

In step 830, an analysis of thermal response of the least thermallyresponsive RVE of the DMDB (smallest and deepest defect) is performed.In some implementations, the thermal simulation employs finite elementanalysis. As will be understood by those of skill in the art, finiteelement analysis is a way to find approximate solution to boundary valueproblems for physical systems that involve partial differentialequations. Heat flow is characterized by partial differential equationsof this type and finite element analysis is often employed in providingsolutions in this field. Finite element analysis includes the use ofmesh generation techniques for dividing a complex problem into smallelements, as well as the use of a finite element simulation thatdetermines solutions to sets of equations for each of the finiteelements as well as a global solution to the entire domain. Followingcompletion of the thermal simulation of the selected least thermallyresponsive RVE, in step 840, the OAP module 138 determines, based on theinput parameters and thermal analysis, new optimized heating parameterssuch as, but not limited to ΔH_(f), ΔH_(p), Δt parameters, in theexample being discussed, in order to achieve a maximum temperaturecontrast during data acquisition.

The optimization of the heating parameters is iterative and the methodperforms a certain number of iterations before outputting optimizedvalues. Accordingly, in step 850 it is determined whether the number ofiterations performed thus far has reached a selectable threshold(MaxIterations). If MaxIterations has not been reached, the processflows back from step 840 to step 820. Alternatively, if MaxIterationshas been reached, in step 860 it is determined whether the value for thedetermined maximum temperature contrast (ΔT) remains lower than theinfrared camera sensitivity. If ΔT is lower than the camera sensitivity,in step 870, the OAP module 138 outputs: 1) the smallest diameterexpected to be detectable for a given depth; 2) the smallest expectedthickness detectable for a given depth; and 3) the greatest expecteddepth detectable within the breadth of a defect for a given defectdiameter. If ΔT is above the threshold, in step 880 the OAP moduleoutputs the current optimized values for heating parameters (e.g.,heating mode, ΔH_(f), ΔH_(p), Δt) from the last iteration of the method.

Returning to FIG. 5 , the accumulated data entered or generated in steps510, 520, 530 and 540 are used as inputs in step 550, in which virtualthermograph module 134 executes a transient thermal analysis (TTA)simulation that outputs ‘virtual’ thermographs for each element (N) inthe DMDB. More specifically, as schematically illustrated in FIG. 9 ,the TTA simulator receives as inputs all of the elements in thedefective microstructure database 910 and the combined operator input,internally-generated parameters, boundary conditions and output of theOAP module (“combined inputs”). The TTA simulation is a parametric,mathematical model that can be implemented using finite elementanalysis. In such a finite element analysis, N separate analyses arecarried out corresponding to the N RVEs contained in the DMDB 910. Theoutput of each finite element analysis is a transient ‘virtual’thermograph of the outer surface of a structural element, i.e., a set ofgraphs showing thermal response of the surface over time. Generally, theexpected accuracy of the finite analysis depends on the number ofelements in the DMDB 910 (i.e., the value of N), with higher values of Nimproving the expected accuracy

The thermograph data is output and formatted as a matrix F_(ijk) in avisual thermograph database (VTDB) 940, where i represents the ithcamera pixel element, j represents the jth time increment, and krepresents the kth RVE. FIG. 10A provides an illustration of the datastructure of matrix F_(ijk). In the figure F_(ij1) represents all of theelements of matrix pertaining to the first RVE (k=1). Nested withinF_(ij1) are entries F_(i11) within which, in turn, are nested elementsF₁₁₁ through F_(n11). Elements F₁₁₁ through F_(n11) represent all of therecorded pixels during the first time increment for the first RVE (j=1,k=1). Accordingly, for each of the N RVEs there are associated m timeincrements, and during each time increment, n pixel values aregenerated. FIG. 10B is a schematic perspective illustration of athermograph at a given time increment, indicating how the thermographsfor a given RVE can be envisioned as a block of m thermographs, witheach thermograph having n pixels. As can be discerned, a high resolutionsimulation can generate a large amount of data. However, as the trainingsystem 130 performs analysis offline, there is no fixed limit to theresources that can be allocated to the transient thermal analysis.Moreover, the resolution level can be varied by the operator ifresources or efficiency are limiting factor in a particular scenario.

With a database of thermographs of sufficient precision and accuracy, itis possible to compare thermographs of a composite structure acquiredduring inspection runs in the field with thermographs in the database toidentify any defects present in the structure. However, it iscomputationally expensive to compare entire images for matching, andeven more so to compare the evolution of images (transient response)over time. One way to solve this problem is by training the system tocorrelate the virtual thermographs with the parameters of the RVEs fromwhich they are derived. In this way, when thermographs are acquired inthe field, they can be analyzed without having to search through animage database.

Therefore, in step 560 of the training method, an expert system istrained by a machine learning process to correlate the images of thevirtual thermograph database with the parameters of the RVEs from whichthey are derived. In some implementations, the expert system module 136of training system 130 employs a neural network algorithm, shown in FIG.11 , as the machine learning technique. Neural network 1100 includes aninput layer 1110, one or more hidden layers 1120 and an output layer1130. The input layer 1110 includes all of the pixels of a virtualthermograph of the VTDB for a given RVE at a particular time increment,and the output layer 1130 includes the parameters of the same RVEincluding its position, orientation, defect dimensions, defect type andentrapped media. The neural network correlates the input layer 1110 tothe output layer 1130 by use of one or more hidden layers 1120. Each ofthe inputs in the input layer 1110 is multiplied by coefficient factorsin the hidden layer(s) 1120 to yield the output layer 1130. Thecoefficients of the hidden layers 1120 are determined by a process ofbackward propagation in which a cost function is minimized. This yieldsan optimized correlation between the virtual thermographs and the RVEparameters. The expert system module 136 stores the coefficients forfurther use. After the expert system training is complete, the trainingmethod ends in step 570.

Real-Time Inspection Method

Flow charts of the sub-parts of a real time inspection method 1200performed by the online computer system 120 and inspection apparatus110, respectively, are shown in FIG. 12 . As noted above, the expertsystem is generated and stored off-site at a remotely located facility.In order for operators at a field site to perform a structuralinspection to be able to utilize the expert system, access to the expertsystem at the onsite location is required. In a first step 1205, anoperator obtains access to the expert system either by logging into anexpert system server over a network using online computer system oralternatively, by directly downloading the expert system algorithm andstored data from the training system 130 onto the online computer system120. Additionally, the expert system can be downloaded by a using astorage medium such as a flash drive. In step 1210, the online computersystem uploads optimized acquisition parameters from the OAP module 138of training system 130. In a following step 1215, the online computersystem 120 transmits the optimized acquisition parameters to thetransceiver 350 of inspection apparatus 110.

In step 1255, inspection apparatus 110 receives the optimizedacquisition parameters from online computer system 120. Using theacquired parameters, in step 1260, the controller 330 of inspectionapparatus 110 configures heating and acquisition parameters foroperating the heating device 310 and infrared camera 320. Uponconfiguration, the inspection apparatus is configured to apply radiationand capture infrared radiation for the smallest and deepest defect thatis within the detection capability of the infrared camera, so that theinspection apparatus as a whole has maximum sensitivity for the givenhardware capabilities. In step 1265, the inspection apparatus performsan inspection in which a section of an inspected surface is heated byheating device 310 and infrared radiation acquired by infrared camera320. During inspection, the inspection apparatus can be fixed inposition to inspect a specific area of a structure, or the inspectionapparatus can be controlled to move in a particular trajectory toinspect different areas or the entire surface of a structure. In realtime or approximate real time, in step 1270, the controller compiles theinfrared radiation data acquired by the infrared camera and transmitsthe data in the form of thermographs to computer system 120 viatransceiver 350.

Computer system 120 receives the thermographs in step 1220, and in step1225, performs real-time quantification of defects in the inspectedstructure based on the acquired thermographs. Step 1225 is schematicallyillustrated in FIG. 13 which illustrates a thermograph 1310 input toexpert system 1320. Expert system 1320 in this case is the moduleexecuted on computer system 120 (as opposed to the training system 130)and, as noted above, can represent a client of an expert system server,or a software module executed on computer system 120 that emulatesaspects of the expert system module 136 of the training system 130. Insome implementations, expert system 1320 can be a copy of the expertsystem module 136 uploaded from the training system 130. Expert system1320 applies the correlations obtained from the training system 130 tothe acquired thermograph and outputs a defect parameter vector includingthe elements described above with reference to FIG. 7 . The defectparameter vector identifies the defect in terms of its type, size,depth, orientation and entrapped media. The online computer system then,in step 1230, generates a defect quantification report that includes thethermographs acquired in real time and the characteristics of anydetected defects.

The disclosed apparatus, system and methods for inspecting structuresusing quantitative infrared thermography provide several advantageousfeatures. The system and methods are easy to implement as, in someembodiments, the inspection apparatus can move automatically around andalong the inspected structure, reducing manual inspection procedures. Inaddition, embodiments of the inspection apparatus are designed toprogress rapidly over inspected structures, further reducinginterventions in the inspection process. The disclosed system alsodelivers inspection results in real-time, allowing the possibility ofinitiating remedial measures onsite to remove serious defects. Theinspection apparatus is contact free and relatively cost effective; theinfrared camera is the highest expense in most implementations.Moreover, the system provides unbiased configuration of the inspectionapparatus since optimization parameters for data acquisition aredetermined by the system independently from the operator. Likewise,inspection results are unbiased as they are generated independently fromhuman expert knowledge or expertise. The large number of virtual samples

While the apparatus, system and methods disclosed herein areparticularly intended to be used for composite inspection and defectdetection, with suitable modifications, the inventive techniques can beapplied to other materials.

It is to be understood that any structural and functional detailsdisclosed herein are not to be interpreted as limiting the apparatus,system and methods, but rather are provided as a representativeembodiment and/or arrangement for teaching one skilled in the art one ormore ways to implement the methods.

It is to be further understood that like numerals in the drawingsrepresent like elements through the several figures, and that not allcomponents and/or steps described and illustrated with reference to thefigures are required for all embodiments or arrangements

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising”, when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

Terms of orientation are used herein merely for purposes of conventionand referencing, and are not to be construed as limiting. However, it isrecognized these terms could be used with reference to a viewer.Accordingly, no limitations are implied or to be inferred.

Also, the phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” or “having,” “containing,” “involving,” andvariations thereof herein, is meant to encompass the items listedthereafter and equivalents thereof as well as additional items.

While the invention has been described with reference to exemplaryembodiments, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted forelements thereof without departing from the scope of the invention. Inaddition, many modifications will be appreciated by those skilled in theart to adapt a particular instrument, situation or material to theteachings of the invention without departing from the essential scopethereof. Therefore, it is intended that the invention not be limited tothe particular embodiment disclosed as the best mode contemplated forcarrying out this invention, but that the invention will include allembodiments falling within the scope of the appended claims.

What is claimed is:
 1. A method of quantitatively inspecting a surfaceof a structure for defects from which infrared thermographs are acquiredby an inspection apparatus, the method comprising: obtaining a set ofcorrelations between parameters of modeled structural defects andsimulated thermographs of the modeled structural defects, and optimalacquisition parameters for configuring the inspection apparatus foracquiring infrared thermograph data from the structure; communicatingthe acquisition parameters to the inspection apparatus; receivinginfrared thermograph data acquired from the structure from theinspection apparatus; analyzing the received infrared thermograph datausing the obtained correlations; and determining parameters of defectswithin the structure based on the analysis of the received infraredthermograph data, wherein the acquisition parameters for configuring theinspection apparatus include heating parameters including at least oneof a heating mode, a heating time, a target heat flux level for applyingheat to the structure, and an acquisition time detecting infraredradiation from the structure.
 2. The method of claim 1, wherein thedetermined parameters of defects within the structure include alocation, a depth, a defect type and an entrapped media type.
 3. Themethod of claim 1, wherein the acquisition parameters are communicatedto the inspection apparatus and the infrared thermograph data isreceived fro-m the inspection apparatus via wireless communication. 4.The method of claim 3, wherein the received infrared thermograph dataare analyzed using the obtained correlations employing a trained neuralnetwork.
 5. A method of quantitatively inspecting a surface of astructure for defects from which infrared thermographs are acquired byan inspection apparatus, the method comprising: obtaining a set ofcorrelations between parameters of modeled structural defects andsimulated thermographs of the modeled structural defects, and optimalacquisition parameters for configuring the inspection apparatus foracquiring infrared thermograph data from the structure; communicatingthe acquisition parameters to the inspection apparatus; receivinginfrared thermograph data acquired from the structure from theinspection apparatus; analyzing the received infrared thermograph datausing the obtained correlations; and determining parameters of defectswithin the structure based on the analysis of the received infraredthermograph data; wherein the acquisition parameters are communicated tothe inspection apparatus and the infrared thermograph data is receivedfrom the inspection apparatus via wireless communication; and whereinthe inspection apparatus includes a clamp element for removably fixingthe apparatus in proximity to the surface of the structure.
 6. Themethod of claim 5, wherein the inspection apparatus further includesrotatable and translatable wheels fixed to ends of the clamp element. 7.A method of quantitatively inspecting a surface of a structure fordefects from which infrared thermographs are acquired by an inspectionapparatus, the method comprising: obtaining a set of correlationsbetween parameters of modeled structural defects and simulatedthermographs of the modeled structural defects, and optimal acquisitionparameters for configuring the inspection apparatus for acquiringinfrared thermograph data from the structure; communicating theacquisition parameters to the inspection apparatus; receiving infraredthermograph data acquired from the structure from the inspectionapparatus; analyzing the received infrared thermograph data using theobtained correlations; and determining parameters of defects within thestructure based on the analysis of the received infrared thermographdata, wherein the optimal acquisition parameters are determined based ona material of the structure and environmental conditions at thestructure.